Another great course from edhec business school on portfolio construction. The reason why i am giving 3 is because forum quality and support is below acceptable. What is the point for Claudia to encourage this conversations? We come here to learn practical skills and sometimes need help and support, which does not exist. Great course otherwise.

This is an excellent course for anyone in finance, especially Portfolio and Asset Management professions. This particular segment is the 2nd of 4 parts of the "Investment Management with Python and Machine Learning Specialization".

Unfortunately, the code instructor dropped the ball in this part (compared to the first, "Introduction to Portfolio Construction and Analysis with Python"). The coding lab sessions were app 1/3 the length of Part 1, but the code is much more challenging in Part 2 (and more, not less, instruction is necessary). The instructor briefly skims over a previously completed python workbook, then asks the student to "play around with the code" in order to fully comprehend it. This is rather insulting to many - as indicated by Discussion Forum Comments.

Also, the quizzes contain a number of mismatches between the Python Function library (known as the Edhec Risk Kit) and the questions. The person responsible for answering student questions barely does so with adequate thoroughness and has failed to update much of his erroneous suggestions. I recommend the original instructors commit to fixing the many holes here, and consider assigning a more helpful person to the Discussion Forum - Emanuelle doesn't cut it.

The Python code instructor also should re-record videos for this section - so that we don't need to "play around with his code" in order to understand it.

lab session is way too fast and the questions didnt get answered in week3 quiz

Felt the intro course was better designed. The advanced course did not do a good of explaining the code that was used

This is a fantastic course because it fills the gap between high level theory described in university & research papers and numerical applications thanks to Python programming.

The data we have processed are real market data from official sources covering 5 to 30 years of history in order to draw comprehensive conclusions.

The Python code will be running on your PC so it requires you have installed Python and Jupyter (or full Anaconda suite) on your PC. A good knowledge of Python and Panda dataframes is a required. The sample code, libraries and data files are easily uploaded from the EDHEC site and installed on your PC.

The lab sessions explain how to implement the theoritical formulas into Python procedures. The quizz following each of the 4 modules require some Python coding based on procedures and sample code developped during the lab sessions.

The exercices displayed during the video sessions develop your intuition as they do not require any complex computation or programming to answer.

The first EDHEC MOOC on Python portfolio is a pre-requisite for strating this second MOOC.

I strongly recommends this course to anyone interested in modern portfolio management concepts and applications using Python programming.

I thank Pr Lionel Martinelli and Pr Vijay Vaidyanathan for their teaching methods.

With Best Regards Jean-Louis Bailly

It is my great honor to attend the high quality course Advanced Portfolio Construction and Analysis with Python. Thank the instructors for their professional guidance in the class and classmates, TAs for their hospitality in the forum. The course is one of the best Coursera courses I have taken, which not only provides profound finance knowledge, but also guides on fantastic Python Programming which becomes very useful tool in practical market/ investment analysis. Highly Recommend! Best Regards and blessings.

One of the best courses (Specializations) that Coursera has to offer. Really enjoy video lectures and programing labs. My favourite part is that the course is related to the real-world and addresses the questions of the applicability of recent theoretical developments. Learned modern state of the art technologies and enjoyed playing with the numbers. Programming assignments could have been harder but they are fun anyways.

Good overview on portfolio theory with some of the latest trends (multi-factor models) and Python Lab sessions follow the same logic than the first course, with good tips and good timing.

Loved how this course was presented. It built well off of the first course and provided labs that let me explore the content. I really enjoyed how Lionel and Vijay presented the material.

The course is excellent, one of the best finance courses on coursera, but you should know in advance that you will not have any help from the staff, at least that was my experience.

I like the way instructors explained difficult topic and digest it to simple way. The coding side was also impressive. WIth novice background in Python, I would able to understand.

I preferred the first MOOC where the coding was interactive as well. It was engaging to learn the coding at the same time with the investment knowledge....MOOC2 not as good as MOOC1 in my opinion from an engagement standpoint. But none the less, good knowledge and content in this course.

In comparison to the first class (introduction to portfolio construction and analysis with Python) this one feel more surface level. The coding videos are much shorter and explain much much less. This has extended the time it takes to complete the course because I must look to other sources to explain the material. It also many times difficult to understand the material due to the language barrier. The two in conjunction make it such that to complete this course, you're better off seeking the information elsewhere - which in essence is not what a course should be like. Why pay to understand something when it's not fully explained? But that's just my opinion.

Course is very well organized. Journey from VW to EW to GMV to ERC is a very useful concept in quantitative investment management industry. Use of Python to reverse engineer BLM (Black-Litterman Model) and apply the same code in labs is extremely helpful in perfecting skills.

If possible, have two labs each week. First lab can be based on a small portfolio (3 assets) to perfect theory and validate answers easily. The second lab can be as is.

Overall, thank you both Dr. Martellini and Dr. Vaidyanathan for your effort and high-quality audio visual content. A great job well done.

I highly recommend this course for practitioners and Masters in Financial Engineering and Masters in Finance students.

This Course is really amazing! I appreciated the way the lab-sessions are explained, since, in contrast to the first course, the instructor briefly explains the code forcing you to play around with. In this way, I really improved a lot my coding skills. Furthermore, I found both interesting and helpful even the theoretical part, with Lionel that walks you through difficult concept in a very effective way giving you a solid understanding of the core concepts related to portfolio Construction and performance improvement. Therefore, I strongly suggest this course to everyone interested in learning some practical coding skills in financial field.

I highly recommend this course. The information and explanation is outstanding, and is something that is really applicable, not just theory. The lab instructor Dr. Vijay Vaidyanathan does a good job of explaining each element, but if he took the time like he did in MOOC 1 it would be perfect. The quiz is great because you have to really understand the theory and code in order to answer them, so you can't just skip through the labs and except to pass.

This and the previous MOOC in the specialization has been two of the best courses I have ever had in terms of practicality and effective teaching.

I really loved this course. Lionel and Vijay are extremely personable and have a way of presenting difficult material in an easygoing and straightforward manner. I enjoyed the lab sessions in the first course much better because Vijay took the time to work through the Python functions and demonstrated his thought process, even walked through his mistakes, and told us why an error was raised. I suspect that due to some unfortunate comments by 'smart' people, too 'smart' for their own good, he didn't use this approach in the second course and I feel a little cheated.

Super clear explanations, combining intuitions with rigorous definitions for the pros and cons of each investment strategy. The theory is clear, and the lab sessions introduce you to well-designed Python code, thus the perfect combo for pragmatic learners :)

Note that the Python code is not trivial, and in this MOOC the instructor does not go as deep in the explanations as in the 1st MOOC of this Specialization, so make sure you are familiarized with Python.

Really insightful courses (parts 1 +2)! They are extremely well balanced between practical motivation, theoretical background, and applied Python techniques. I got a brillant overview of why the concepts are created, what the core ideas behind those are, empirical examples, and last, but not least, how to beautifully implement them in Python. The courses serve a large audience from a wide range of backgrounds - from pure techies to pure "businessists".

Great course, interesting material, outstanding instruction. The course covers advanced portfolio construction techniques, but as feedback, it would be nice to see more focus on their practical application, particularly construction and maintenance of datasets such as the ones used in lab exercises. The question where/how to get the data needed to run things covered in class in the "real world" is one to figure out outside of the class.